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A synaptic mechanism for encoding the learned value of action-derived safety.

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  1. [1] § Methods › Fiber photometry ↔ Codes/Whole_Trial_Photometry/Whole Trial GCaMP.ipynb, lines 75–216 · score 0.52 · cue onset, linear, autofluorescence, GCaMP, fit, photometry

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Jupyter notebook · 258 lines · 11 KB · no license · 1 match

  1. # %% [markdown]
  2. # # Calculate GCaMP DFF
  4. # %% [markdown]
  5. # ### 1) Read in the autofluorescence, GCaMP, and shock csv files and return auto, gcamp, and shock pandas DataFrames.
  7. # %%
  8. #import packages
  9. import os as os #os
  10. import pandas as pd #pandas
  11. import numpy as np #numpy
  12. import scipy as scipy #scipy
  13. import matplotlib.lines as mlines #matplotlib
  14. import matplotlib.pyplot as plt #matplotlib
  15. plt.style.use('ggplot') #emulate ggplot from R
  16. #% matplotlib inline
  17. #view plots in jupyter notebook
  19. # %% [markdown]
  20. # #### *User Input Required Below*
  22. # %%
  23. #***change working directory, ID, session, and number of trials****
  24. #***These are the only details you need to change to run the whole script***
  26. #IMPORTANT - to change working directory, use os.chdir(path)
  27. os.chdir('C:\\')
  28. working_directory = os.getcwd()
  29. print(working_directory)
  30. ID = ''
  31. session = ''
  34. # %% [markdown]
  35. # #### *User Input Required Below*
  37. # %%
  38. #read in the autofluorescence, GCaMP, and cue csv files and return auto, gcamp, and shock pandas DataFrames
  39. #IMPORTANT - to change file name, format as ('file name.csv')
  40. auto = pd.read_csv(ID + '_' + session +'_AF.csv')
  41. gcamp = pd.read_csv(ID + '_' + session + '_GC.csv')
  42. shock = pd.read_csv(ID + '_' + session + '_cue.csv')
  44. # %% [markdown]
  45. # ### 2) Combine the time column and the auto, gcamp, and shock d0 columns to create a master pandas DataFrame. Write out the master pandas DataFrame as a csv file to the working directory.
  47. # %%
  48. #make auto, gcamp, and shock column headings lowercase
  49. auto.columns = auto.columns.str.lower()
  50. gcamp.columns = gcamp.columns.str.lower()
  51. shock.columns = shock.columns.str.lower()
  52. #absolute value of shock d0 column values
  53. shock.d0 = shock.d0.abs()
  54. #combine time column and auto, gcamp, and shock d0 columns to create master pandas DataFrame
  55. master = pd.concat([auto['time'], auto['d0'], gcamp['d0'], shock['d0']], axis = 1, keys = ['time', 'auto', 'gcamp', 'shock'])
  56. #write out master as a csv file to working directory
  57. master.to_csv(ID + '_' + session + '_master.csv')
  59. # %% [markdown]
  60. # ### 3) Determine the data range for the calculations. Create a master_input pandas DataFrame.
  62. # %%
  63. #determine the rows in which shock occurs
  64. shock_rows = master.loc[master.shock > .75].index[:].tolist()
  65. #determine the rows in which shock onset occurs
  66. shock_onset_rows = [shock_rows[0]]
  67. for i in range(1, len(shock_rows)):
  68. if shock_rows[i] > shock_rows[i - 1] + 1:
  69. shock_onset_rows.append(shock_rows[i])
  71. file_num = 1 #set file number to start at 1 initially
  72. shock_onset_rows = shock_onset_rows[0:len(shock_rows)]
  73. print('shock_onset_rows =' , shock_onset_rows)
  75. # %% [markdown]
  76. # #### *User Input Required Below*
  78. # %%
  79. for num in shock_onset_rows:
  80. #create shock_onset
  81. shock_onset = num #IMPORTANT - can change number to any of the numbers in shock_onset_rows
  82. if shock_onset in shock_onset_rows:
  83. print('shock_onset =', shock_onset)
  84. else:
  85. raise ValueError('shock_onset not found in shock_onset_rows')
  86. #create begin_input
  87. begin_input = shock_onset - 120 #IMPORTANT - can change number to any number of rows before shock onset
  88. print('begin_input =', begin_input)
  89. #create last_row
  90. last_row = len(master) - shock_onset - 1
  91. #create end_inputD7_Ext3_091021_AF
  92. end_input = shock_onset + 240 #IMPORTANT - can change number to any number of rows after shock onset or to last_row for the last row in the data set
  93. print('end_input =', end_input)
  94. #create file name for future files
  95. file_num_str = str(file_num) #change file number to string so it can be added to the file name
  96. file_name = ID + '_' + session + '_Stim_T'+ file_num_str #IMPORTANT - to change file name, format as 'file name8
  97. #create master_input pandas DataFrame
  98. master_input = master[begin_input:end_input + 1]
  100. ##### 4) Determine auto/gcamp linear trendline equations. Create auto/gcamp scatter plots with auto/gcamp linear trendlines. Save the auto/gcamp plots as PDFs to the working directory.
  101. #create master_trendlines pandas DataFrame
  102. master_trendlines = master_input.loc[begin_input:shock_onset - 1]
  103. #reset master_trendlines row index
  104. master_trendlines = master_trendlines.reset_index(drop = True)
  105. #create x_master_trendlines (ranging from 1 to # rows in master_trendlines) pandas DataFrame
  106. x_range_master_trendlines = master_trendlines.axes[0] - (master_trendlines.axes[0][0] - 1)
  107. x_master_trendlines = pd.DataFrame({'x': x_range_master_trendlines})
  108. #add x_master_trendlines to master_trendlines
  109. master_trendlines = pd.concat([x_master_trendlines, master_trendlines], axis=1, join='inner')
  111. #determine auto linear trendline equation
  112. from pylab import *
  113. (a, b) = polyfit(master_trendlines.x, master_trendlines.auto, 1)
  114. auto_linear_trendline_equation = 'y = ' + str(round(a, 5)) + 'x + ' + str(round(b, 5))
  116. #create auto scatter plot with auto linear trendline
  117. plt.scatter(master_trendlines.x, master_trendlines.auto, color = 'blue', s = 10)
  118. auto_trendline_values = polyval([a,b], master_trendlines.x)
  119. plt.plot(master_trendlines.x, auto_trendline_values, linewidth = 3, color = 'red')
  120. plt.title(auto_linear_trendline_equation, fontsize = 10, y = 0.9)
  121. plt.xlabel('x')
  122. plt.ylabel('autofluorescence')
  123. #save auto scatter plot as PDF to working directory
  124. plt.savefig('auto_plot_' + file_name + '.pdf')
  126. #determine gcamp linear trendline equation
  127. from pylab import *
  128. (c, d) = polyfit(master_trendlines.x, master_trendlines.gcamp, 1)
  129. gcamp_linear_trendline_equation = 'y = ' + str(round(c, 5)) + 'x + ' + str(round(d, 5))
  131. #create gcamp scatter plot with gcamp linear trendline
  132. plt.scatter(master_trendlines.x, master_trendlines.gcamp, color = 'blue', s = 10)
  133. gcamp_trendline_values = polyval([c,d], master_trendlines.x)
  134. plt.plot(master_trendlines.x, gcamp_trendline_values, linewidth = 3, color = 'red')
  135. plt.title(gcamp_linear_trendline_equation, fontsize = 10, y = 0.9)
  136. plt.xlabel('x')
  137. plt.ylabel('gcamp')
  138. #save gcamp scatter plot as PDF to working directory
  139. plt.savefig('gcamp_plot_' + file_name + '.pdf')
  141. #create master_calculations pandas DataFrame
  142. master_calculations = master_input
  143. #reset master_calculations row index
  144. master_calculations = master_calculations.reset_index(drop = True)
  145. #create x_master_calculations (ranging from 1 to # rows in master_calculations) pandas DataFrame
  146. x_range_master_calculations = master_calculations.axes[0] - (master_calculations.axes[0][0] - 1)
  147. x_master_calculations = pd.DataFrame({'x': x_range_master_calculations})
  148. #add x_master_calculations to master_calculations
  149. master_calculations = pd.concat([x_master_calculations, master_calculations], axis = 1, join = 'inner')
  151. #create auto_trendline_y pandas DataFrame
  152. auto_y_list = []
  153. for x in x_range_master_calculations:
  154. y = a*x + b
  155. auto_y_list.append(y)
  156. auto_y_array = np.array(auto_y_list)
  157. auto_trendline_y = pd.DataFrame({'auto_trendline_y': auto_y_array})
  158. #add auto_trendline_y to master_calculations
  159. master_calculations = pd.concat([master_calculations, auto_trendline_y], axis = 1, join = 'inner')
  160. #create gcamp_trendline_y pandas DataFrame
  161. gcamp_y_list = []
  162. for x in x_range_master_calculations:
  163. y = a*x + d
  164. gcamp_y_list.append(y)
  165. gcamp_y_array = np.array(gcamp_y_list)
  166. gcamp_trendline_y = pd.DataFrame({'gcamp_trendline_y': gcamp_y_array})
  167. #add gcamp_trendline_y to master_calculations
  168. master_calculations = pd.concat([master_calculations, gcamp_trendline_y], axis = 1, join = 'inner')
  170. #subtract auto_trendline_y from auto to create auto_fit column in master_calculations
  171. master_calculations['auto_fit'] = master_calculations['auto'] - master_calculations['auto_trendline_y']
  172. #subtract gcamp_trendline_y from gcamp to create gcamp_fit column in master_calculations
  173. master_calculations['gcamp_fit'] = master_calculations['gcamp'] - master_calculations['gcamp_trendline_y']
  175. #add gcamp_trendline y-intercept to auto_fit to create auto_fit column in master_calculations
  176. master_calculations['auto_final'] = d + master_calculations['auto_fit']
  177. #add gcamp_trendline y-intercept to gcamp_fit to create gcamp_fit column in master_calculations
  178. master_calculations['gcamp_final'] = d + master_calculations['gcamp_fit']
  180. #calculate delta f/f (dff) and create dff column in master_calculations
  181. master_calculations['dff']= ((master_calculations['gcamp_final'] - master_calculations['auto_final'])/master_calculations['auto_final'])*100
  183. #write out master_calculations as a csv file to working directory
  184. master_calculations.to_csv('master_calculations_' + file_name + '.csv')
  186. #determine the rows in which shock occurs
  187. shock_rows_dff = master_calculations.loc[master_calculations.shock < 1].index[:].tolist()
  188. #determine the rows in which shock onset occurs
  189. shock_onset_rows_dff = [shock_rows_dff[0]]
  190. for i in range(2, len(shock_rows_dff)):
  191. if shock_rows_dff[i] > shock_rows_dff[i - 1] + 1:
  192. shock_onset_rows_dff.append(shock_rows_dff[i])
  193. #determine the times in which shock onset occurs
  194. shock_onset_time_dff = list(master_calculations.time.loc[shock_onset_rows_dff])
  195. #create dff line plot
  196. fig, ax = plt.subplots()
  197. ax.plot(master_calculations.time, master_calculations.dff, color = 'blue')
  198. ax.set_xlabel('time (sec)')
  199. ax.set_ylabel('delta f/f')
  200. x = 0
  201. while x < len(shock_onset_time_dff):
  202. ax.annotate(' ', xy =(shock_onset_time_dff[x], min(master_calculations.dff)), arrowprops = dict(facecolor = 'black', shrink = 0.05))
  203. x = x + 1
  204. arrow = mlines.Line2D([], [], color = 'black', marker = '^', markersize = 12, label = 'cue onset')
  205. ax.legend(handles = [arrow])
  206. #while x < len(shock_onset_time_dff):
  207. #ax.annotate(' ', xy =([shock_onset_time_dff[x] + 30], min(master_calculations.dff)), arrowprops = dict(facecolor = 'yellow', shrink = 0.05))
  208. #x = x + 1
  209. #arrow2 = mlines.Line2D([], [], color = 'yellow', marker = '^', markersize = 12, label = 'cue offset')
  210. #ax.legend(handles = [arrow, arrow2])
  211. #save dff line plot as PDF to working directory
  212. fig.savefig('dff_plot_' + file_name + '.pdf')
  214. #Necessary for iterative code
  215. matplotlib.pyplot.close('all')
  216. file_num += 1 #increase file number by 1 for next file
  218. # %%
  219. #Concatenation step
  220. #Make sure you have number_trials set to the correct number at the beginning of the script
  222. #number_trials = [x+1 for x in range(0,len(shock_rows))] #change range number to reflect your number of trials
  223. #print(number_trials)
  225. rows = [x+1 for x in range(0,len(shock_onset_rows))] #set at beginning of script
  226. file_name = 'master_calculations_' + ID + '_' + session + '_Stim_T' + str(rows[0]) + '.csv' #Changes file name for each animal based on settings at begining
  228. df = pd.read_csv(file_name)
  229. df.drop('Unnamed: 0', axis=1, inplace=True)
  230. df.head(3)
  232. total_dff = pd.DataFrame()
  233. total_dff[str(rows[0])] = df.dff
  234. total_dff.head(3)
  236. for row in rows:
  237. file_name = 'master_calculations_' + ID + '_' + session + '_Stim_T' + str(row) + '.csv' #Changes file name for each animal
  238. df = pd.read_csv(file_name)
  239. df.drop('Unnamed: 0', axis=1, inplace=True)
  240. total_dff[str(row)] = df.dff
  242. total_dff.head(5)
  244. total_dff['avg'] = total_dff.mean(axis=1)
  246. total_dff.to_csv(ID + '_' + session + '_final.csv') #Save to new file.
  248. # %%
  249. print(d)
  251. # %%
  254. # %%
  257. # %%

Whole Trial GCaMP.ipynb at commit ab93d0d, no license · at the source

Overview

Authors: Emma E Macdonald1, Jun Ma1,2, Di Liu1, Kai Yu1, Rachel A Walker1, Michael E Authement3, Yan Leng1, Hannah C Goldbach3, Guochuan Li4, Yulong Li4, Veronica A Alvarez3, Bruno B Averbeck5, Mario A Penzo1
ORCID iDs: Rachel A Walker, Hannah C Goldbach, Guochuan Li, Yulong Li, Veronica A Alvarez, Bruno B Averbeck, Mario A Penzo
  1. Section on the Neural Circuits of Emotion and Motivation, National Institute of Mental Health, Bethesda, MD USA
  2. Present Address: Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, China
  3. Section on Neurobiology of Compulsive Behaviors, National Institute of Mental Health, Bethesda, MD USA
  4. State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
  5. Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD USA
Institutions: National Institutes of Health (United States); National Institute of Mental Health (United States); Xuzhou Medical College (China); Jiangsu Province Key Laboratory of Anesthesiology (China); Peking University (China); State Key Laboratory of Membrane Biology
Journal: Nature communications, volume 17, issue 1, article 4916
Dates: received 2 September 2025; accepted 25 May 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73906-3 · PMID 42243099 · PMCID PMC13237380 · OpenAlex W4412951732
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.04.668532
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Motivation, Cellular neuroscience, Neural circuits, Molecular neuroscience
MeSH: Avoidance Learning*, Midline Thalamic Nuclei*, Nucleus Accumbens*, Synapses*, Animals, Cholinergic Neurons, Dopamine, Interneurons, Learning, Male, Mice, Mice, Inbred C57BL, Motivation, Neural Pathways, Neuronal Plasticity, Receptors, AMPA (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Intramural NIH HHS (ZIA MH002928); U.S. Department of Health &amp; Human Services | National Institutes of Health (ZIA MH002928, 1ZIAMH002950)
Citations: cited by 1 paper (Europe PMC); 90 references in the paper

Abstract

Adaptive behavior requires that behaviorally relevant signals gain access to neural circuits guiding action. The thalamus has long been proposed to regulate information flow to cortical and subcortical systems, yet whether it also tracks internally generated goal signals remains unclear. Here, we show that neurons in the paraventricular thalamus (PVT) projecting to the nucleus accumbens (NAc) encode the motivational value of safety during active avoidance. As mice learn to avoid threat, PVT→NAc neurons develop a signal that emerges selectively at successful avoidance, is experience-dependent, and diminishes following outcome devaluation. Selective silencing of the PVT→NAc pathway at safety onset reduces the motivational value assigned to safety without impairing action–outcome learning. Mechanistically, PVT input engages cholinergic interneurons (CIN) in the NAc to regulate dopamine release via synaptic potentiation mediated by GluA2-lacking AMPA receptors at PVT–CIN synapses. Disrupting this plasticity reduces the motivational impact of safety. These findings identify a thalamostriatal mechanism through which learned goals gain stable access to motivational circuitry.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Penzolab/Data-analysis-of-Two-way-active-avoidance-task

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Languages: R (13)
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At the source: github.com/Penzolab/Data-analysis-of-Two-way-active-avoidance-task.git

Zenodo 12707790

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laxace33/penzo-lab-files-for-ma-omalley-et-al-2024

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Commit: ab93d0db07a305fd61a10a4edb86cad45145e57e, 10 July 2024
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Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
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At the source: github.com/laxace33/Penzo-Lab-files-for-Ma-OMalley-et-al-2024/tree/Publication

Code availability

R code used to analyze active avoidance behavior, and photometric signal is available at the following repository: https://github.com/Penzolab/Data-analysis-of-Two-way-active-avoidance-task.git. 10.5281/zenodo.12707790.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 41 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

The datasets generated in this study have been deposited in the Zenodo database under accession code DIO:10.5281/zenodo.20031828. All relevant processed data are also provided in the Supplementary Information/Source Data file. The datasets used in this study are fully publicly accessible without restriction. Source data are provided with this paper.

R code used to analyze active avoidance behavior, and photometric signal is available at the following repository: https://github.com/Penzolab/Data-analysis-of-Two-way-active-avoidance-task.git. 10.5281/zenodo.12707790.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 4 keywords, 16 MeSH terms, 2 funders, 88 references.

Cite

This paper

Macdonald, E. E., Ma, J., Liu, D., Yu, K., Walker, R. A., Authement, M. E., Leng, Y., Goldbach, H. C., Li, G., Li, Y., Alvarez, V. A., Averbeck, B. B., & Penzo, M. A. (2026). A synaptic mechanism for encoding the learned value of action-derived safety. Nature communications, 17(1), 4916. https://doi.org/10.1038/s41467-026-73906-3

BibTeX

@article{macdonald2026synaptic,
author = {Macdonald, Emma E and Ma, Jun and Liu, Di and Yu, Kai and Walker, Rachel A and Authement, Michael E and Leng, Yan and Goldbach, Hannah C and Li, Guochuan and Li, Yulong and Alvarez, Veronica A and Averbeck, Bruno B and Penzo, Mario A},
title = {{A synaptic mechanism for encoding the learned value of action-derived safety}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {4916},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73906-3},
url = {https://doi.org/10.1038/s41467-026-73906-3},
pmid = {42243099},
pmcid = {PMC13237380}
}

RIS

TY - JOUR
AU - Macdonald, Emma E
AU - Ma, Jun
AU - Liu, Di
AU - Yu, Kai
AU - Walker, Rachel A
AU - Authement, Michael E
AU - Leng, Yan
AU - Goldbach, Hannah C
AU - Li, Guochuan
AU - Li, Yulong
AU - Alvarez, Veronica A
AU - Averbeck, Bruno B
AU - Penzo, Mario A
TI - A synaptic mechanism for encoding the learned value of action-derived safety
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/04
VL - 17
IS - 1
SP - 4916
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73906-3
UR - https://doi.org/10.1038/s41467-026-73906-3
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
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"family": "Macdonald",
"given": "Emma E"
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"family": "Walker",
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{
"family": "Leng",
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},
{
"family": "Goldbach",
"given": "Hannah C"
},
{
"family": "Li",
"given": "Guochuan"
},
{
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{
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"PMID": "42243099",
"PMCID": "PMC13237380",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73906-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
4
]
]
}
}

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